Causal-aware Large Language Models: Enhancing Decision-Making Through Learning, Adapting and Acting

TL;DR

This paper introduces Causal-aware Large Language Models that incorporate structural causal models into decision-making, enabling better reasoning, adaptation, and policy-making in complex environments through a learning-adapting-acting framework validated on diverse tasks.

Contribution

The paper presents a novel framework integrating structural causal models with LLMs for decision-making, including methods for extracting, updating, and utilizing causal knowledge.

Findings

Improved decision accuracy across 22 tasks in Crafter game

Effective causal knowledge extraction and updating demonstrated

Enhanced environment understanding and policy efficiency achieved

Abstract

Large language models (LLMs) have shown great potential in decision-making due to the vast amount of knowledge stored within the models. However, these pre-trained models are prone to lack reasoning abilities and are difficult to adapt to new environments, further hindering their application to complex real-world tasks. To address these challenges, inspired by the human cognitive process, we propose Causal-aware LLMs, which integrate the structural causal model (SCM) into the decision-making process to model, update, and utilize structured knowledge of the environment in a ``learning-adapting-acting" paradigm. Specifically, in the learning stage, we first utilize an LLM to extract the environment-specific causal entities and their causal relations to initialize a structured causal model of the environment. Subsequently,in the adapting stage, we update the structured causal model through external feedback about the environment, via an idea of causal intervention. Finally, in the acting stage, Causal-aware LLMs exploit structured causal knowledge for more efficient policy-making through the reinforcement learning agent. The above processes are performed iteratively to learn causal knowledge, ultimately enabling the causal-aware LLMs to achieve a more accurate understanding of the environment and make more efficient decisions. Experimental results across 22 diverse tasks within the open-world game ``Crafter" validate the effectiveness of our proposed method.